Friend Ranking in Online Games via Pre-training Edge Transformers

• DOI: 10.48550/arxiv.2302.10043
• Publication Date: 2023-07-18
• Authors: Liang Yao; Jiazhen Peng; Shenggong Ji; Qiang Liu; Hongyun Cai; Feng He; Xu Cheng
• Keywords: Social and Information Networks (cs.SI); FOS: Computer and information sciences; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; Computer Science - Social and Information Networks; 02 engineering and technology

Accepted by the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023)<br/>Friend recall is an important way to improve Daily Active Users (DAU) in online games. The problem is to generate a proper lost friend ranking list essentially. Traditional friend recall methods focus on rules like friend intimacy or training a classifier for predicting lost players' return probability, but ignore feature information of (active) players and historical friend recall events. In this work, we treat friend recall as a link prediction problem and explore several link prediction methods which can use features of both active and lost players, as well as historical events. Furthermore, we propose a novel Edge Transformer model and pre-train the model via masked auto-encoders. Our method achieves state-of-the-art results in the offline experiments and online A/B Tests of three Tencent games.<br/>